Knowledge-Based Decision Support Systems for Personalized u -lifecare Big Data Services

The emergence of information and communications technology (ICT) and rise in living standards necessitate knowledge-based decision support systems that provide services anytime and anywhere with low cost. These services assist individuals for making right decisions regarding lifestyle choices (e.g., dietary choices, stretching after workout, transportation choices), which may have a significant impact on their future health implications that may lead to medical complications and end up with a chronic disease. In other words, the knowledge-based services help individuals to make a personal and conscious decision to perform behaviour that may increase or decrease the risk of injury or disease. The main aim of this chapter is to provide personalized ubiquitous lifecare (u-lifecare) services based on users’ generated big data. We propose a platform to acquire knowledge from diverse data sources and briefly explain the potential underlying technology tools. We also present a case study to show the interaction among the platform components and personalized services to individuals.

[1]  Thomas F. Cuddihy,et al.  Empowering Sedentary Adults to Reduce Sedentary Behavior and Increase Physical Activity Levels and Energy Expenditure: A Pilot Study , 2015, International journal of environmental research and public health.

[2]  Sungyoung Lee,et al.  Multimodal hybrid reasoning methodology for personalized wellbeing services , 2016, Comput. Biol. Medicine.

[3]  Mohsine Eleuldj,et al.  OpenStack: Toward an Open-source Solution for Cloud Computing , 2012 .

[4]  Tim Dallas,et al.  Feature Selection and Activity Recognition System Using a Single Triaxial Accelerometer , 2014, IEEE Transactions on Biomedical Engineering.

[5]  Erhard Rahm,et al.  Data Cleaning: Problems and Current Approaches , 2000, IEEE Data Eng. Bull..

[6]  Vipin Kumar,et al.  Trends in big data analytics , 2014, J. Parallel Distributed Comput..

[7]  Luciano Serafini,et al.  An Ontological Framework for Decision Support , 2012, JIST.

[8]  Miguel Ángel Rodríguez-García,et al.  Creating a semantically-enhanced cloud services environment through ontology evolution , 2014, Future Gener. Comput. Syst..

[9]  N. Owen,et al.  'Too Much Sitting' and Metabolic Risk— Has Modern Technology Caught Up with Us? , 2009 .

[10]  Kevin Wilkinson,et al.  Data integration flows for business intelligence , 2009, EDBT '09.

[11]  Matthew Rockloff,et al.  Associations between occupational indicators and total, work-based and leisure-time sitting: a cross-sectional study , 2013, BMC Public Health.

[12]  Sungyoung Lee,et al.  EFM: evolutionary fuzzy model for dynamic activities recognition using a smartphone accelerometer , 2013, Applied Intelligence.

[13]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[14]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[15]  Sungyoung Lee,et al.  SUPAR: Smartphone as a ubiquitous physical activity recognizer for u-healthcare services , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Ron Sun,et al.  Robust Reasoning: Integrating Rule-Based and Similarity-Based Reasoning , 1995, Artif. Intell..

[17]  Sungyoung Lee,et al.  ATHENA: A Personalized Platform to Promote an Active Lifestyle and Wellbeing Based on Physical, Mental and Social Health Primitives , 2014, Sensors.

[18]  Muhammad Fahim,et al.  Tracking the sedentary lifestyle using smartphone: A pilot study , 2016, 2016 18th International Conference on Advanced Communication Technology (ICACT).

[19]  N. Owen,et al.  Physiological and health implications of a sedentary lifestyle. , 2010, Applied physiology, nutrition, and metabolism = Physiologie appliquee, nutrition et metabolisme.

[20]  Jin Xing,et al.  Research on CBR-RBR Fusion Reasoning Model and Its Application in Medical Treatment , 2015, IEEM 2015.

[21]  Michael R Brule,et al.  Big Data in Exploration and Production: Real-Time Adaptive Analytics and Data-Flow Architecture , 2013 .

[22]  Sungyong Lee,et al.  The Mining Minds digital health and wellness framework , 2016, Biomedical engineering online.

[23]  Kim Longfield,et al.  Putting health metrics into practice: using the disability-adjusted life year for strategic decision making , 2013, BMC Public Health.

[24]  Tim Furche,et al.  Data Wrangling for Big Data: Challenges and Opportunities , 2016, EDBT.

[25]  C. Matthews,et al.  Too much sitting: the population health science of sedentary behavior. , 2010, Exercise and sport sciences reviews.

[26]  Giner Alor-Hernández,et al.  A general perspective of Big Data: applications, tools, challenges and trends , 2015, The Journal of Supercomputing.

[27]  Kaoru Hirota,et al.  A Rule-Based Approach to Activity Recognition , 2010, KICSS.

[28]  Thar Baker,et al.  Micro-context recognition of sedentary behaviour using smartphone , 2016, 2016 Sixth International Conference on Digital Information and Communication Technology and its Applications (DICTAP).

[29]  Sungyoung Lee,et al.  On Curating Multimodal Sensory Data for Health and Wellness Platforms , 2016, Sensors.

[30]  Byeong Ho Kang,et al.  Expert-Driven Knowledge Discovery , 2008, Fifth International Conference on Information Technology: New Generations (itng 2008).

[31]  Tom Fawcett,et al.  Data Science and its Relationship to Big Data and Data-Driven Decision Making , 2013, Big Data.

[32]  Katarzyna Wac,et al.  UbiqLog: a generic mobile phone-based life-log framework , 2013, Personal and Ubiquitous Computing.